Merge pull request #285166 from sdgilley/sdg-freshness

freshness update concept-azure-machine-learning-v2

Author: prmerger-automator[bot]

articles/machine-learning/concept-azure-machine-learning-v2.md

@@ -10,7 +10,7 @@ ms.topic: conceptual
 ms.author: sgilley
 author: sdgilley
 ms.reviewer: balapv
-ms.date: 02/27/2024
+ms.date: 08/21/2024
 #Customer intent: As a data scientist, I want to understand the big picture about how Azure Machine Learning works.
 ---
 
@@ -125,6 +125,8 @@ Create a workspace in the studio welcome page by selecting **Create workspace**.
 
 ---
 
+For more detailed information about creating a workspace, see [Manage Azure Machine Learning workspaces in the portal or with the Python SDK (v2)](how-to-manage-workspace.md).
+
 ## Compute
 
 A compute is a designated compute resource where you run your job or host your endpoint. Azure Machine Learning supports the following types of compute:
@@ -180,6 +182,11 @@ For the content of the file, see [compute YAML examples](https://github.com/Azur
 
 ---
 
+For more detailed information about creating compute, see:
+
+* [Create an Azure Machine Learning compute instance](how-to-create-compute-instance.md)
+* [Create an Azure Machine Learning compute cluster](how-to-create-attach-compute-cluster.md)
+
 ## Datastore
 
 Azure Machine Learning datastores securely keep the connection information to your data storage on Azure, so you don't have to code it in your scripts. You can register and create a datastore to easily connect to your storage account, and access the data in your underlying storage service. The CLI v2 and SDK v2 support the following types of cloud-based storage services:
@@ -291,6 +298,8 @@ For more information, see [environment YAML schema](reference-yaml-environment.m
 
 ---
 
+For more detailed information about environments, see [Create and manage environments in Azure Machine Learning](how-to-manage-environments-v2.md).
+
 ## Data
 
 Azure Machine Learning allows you to work with different types of data:
@@ -313,7 +322,7 @@ For most scenarios, you'll use URIs (`uri_folder` and `uri_file`) - a location i
 
 An Azure Machine Learning [component](concept-component.md) is a self-contained piece of code that does one step in a machine learning pipeline. Components are the building blocks of advanced machine learning pipelines. Components can do tasks such as data processing, model training, model scoring, and so on. A component is analogous to a function - it has a name, parameters, expects input, and returns output. 
 
-## Next steps
+## Related content
 
 * [How to upgrade from v1 to v2](how-to-migrate-from-v1.md)
 * [Train models with the v2 CLI and SDK](how-to-train-model.md)